Autonomous epitaxial atomic-layer synthesis via real-time computer vision of electron diffraction

TL;DR

This paper presents a real-time autonomous system that uses computer vision of electron diffraction images to efficiently optimize the synthesis of epitaxial films, significantly reducing experimental iterations.

Contribution

It introduces a closed-loop autonomous method for epitaxial film growth that leverages real-time electron diffraction analysis to optimize synthesis parameters.

Findings

Achieved over 30-fold reduction in experiments needed for optimization.

Demonstrated successful autonomous synthesis of phase-pure epitaxial films.

Extended the workflow potential to other thin film synthesis platforms.

Abstract

Autonomous science platforms which make decisions on the fly are fundamentally changing the outlook for materials development. AI-driven schemes can effectively reduce the total number of iterations needed to arrive at the best stoichiometry for desired properties or optimum synthesis parameters by significant margins. Here, we demonstrate real-time closed-loop autonomous navigation of a multi-dimensional synthesis parameter space for fabricating phase-pure epitaxial films of a metastable functional oxide phase using pulsed laser deposition. Sequential growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision of electron diffraction images at the unit-cell level. Our scheme regularly resulted in > 30-fold reduction in the number of experiments compared to comprehensive parameter-space mapping. The real-time workflow developed here can be readily extended to other thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous semiconductor manufacturing.